From 5439f31d0d9ec0d12752e44c3a8b82048a499707 Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Fri, 28 Jul 2023 19:36:44 +0200
Subject: [PATCH] Add files via upload

---
 todo/optimal_stimulus.py | 81 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 todo/optimal_stimulus.py

diff --git a/todo/optimal_stimulus.py b/todo/optimal_stimulus.py
new file mode 100644
index 0000000..a83e9e2
--- /dev/null
+++ b/todo/optimal_stimulus.py
@@ -0,0 +1,81 @@
+import torch
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+
+mpl.rcParams["text.usetex"] = True
+mpl.rcParams["font.family"] = "serif"
+
+
+# path to NN
+nn = "network_0_seed0_Coignless_83Epoch_2807-1455"
+PATH = f"./trained_models/{nn}.pt"
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+# load and eval model
+model = torch.load(PATH).to(device)
+model.eval()
+print("Full network:")
+print(model)
+print("")
+
+# enter index to plot:
+idx = int(input("Please select layer: "))
+print(f"Selected layer {idx}:")
+assert idx < len(model)
+model = model[: idx + 1]
+
+# random input
+input_img = torch.randn(1, 200, 200).to(device)
+input_img = input_img.unsqueeze(0)
+input_img.requires_grad_(True)  # type: ignore
+
+output_shape = model(input_img).shape
+target_image = torch.zeros(
+    (*output_shape,), dtype=input_img.dtype, device=input_img.device
+)
+
+input_parameter = torch.nn.Parameter(input_img)
+
+# define parameters
+num_iterations: int = 10000
+learning_rate: float = 0.0005
+
+print(
+    (
+        f"Available max positions: f:{target_image.shape[1]} "
+        f"x:{target_image.shape[2]} y:{target_image.shape[3]}"
+    )
+)
+
+# select neuron and plot for all feature maps (?)
+neuron_f = int(input("Please select neuron_f: "))
+neuron_x = target_image.shape[2] // 2
+neuron_y = target_image.shape[3] // 2
+print(f"Selected neuron {neuron_f}, {neuron_x}, {neuron_y}")
+
+
+optimizer = torch.optim.Adam([{"params": input_parameter}], lr=learning_rate)
+# TODO:
+# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
+
+target_image[0, neuron_f, neuron_x, neuron_y] = 1e4
+
+for i in range(num_iterations):
+    optimizer.zero_grad()
+
+    output = model(input_parameter)
+
+    loss = torch.nn.functional.mse_loss(output, target_image)
+    loss.backward()
+
+    if i % 1000 == 0:
+        print(f"{i} : loss={float(loss):.3e} lr={optimizer.param_groups[0]['lr']:.3e}")
+    optimizer.step()
+    # TODO:
+    # scheduler.step(float(loss))
+
+
+# plot image:
+plt.imshow(input_img.squeeze().detach().cpu().numpy(), cmap="gray")
+plt.show(block=True)